All language subtitles for KU PMGT 823 Session 4 (Part B)- Quantitative

Hello, everyone, and welcome to Part 2
of Session 4 in PMGT 823, Project Risk

Management. In Part A, we focused on
qualitative risk analysis, how to

and prioritize risks based on expert
judgment and experience.

Now, in Part B, we will take things a
step further with quantitative risk

analysis. We will explore how to use
numerical tools and models to estimate

overall impact of risks on project
outcomes. Let's get started.

In this part of the module, we will
focus on quantitative risk analysis.

Unlike qualitative analysis, which is
based on judgment and prioritization,

approach uses numerical methods to
measure overall risk exposure.

We will look at techniques like expected
monetary value, decision tree analysis,

and Monte Carlo simulation.

So if you are ready to dive into some
numbers, let's go.

Let's return to the full risk management
process to see where quantitative risk

analysis fits in.

After planning for risk management,
identifying risks, and performing a

qualitative analysis, finally we arrive
at step four that is highlighted here.

This step uses numerical methods to
estimate the combined effects of the

on overall project objectives.

It is especially helpful when decisions
involve significant costs, timelines, or

resource trade -offs similar to the
previous part we have two

important questions here what exactly is
quantitative data and why is it

important in risk analysis well
quantitative data is information you can

count or verify unlike qualitative data
like i drink coffee every day

quantitative data tells us exactly how
much or how often

For example, I drink four cups of coffee
per day or consume 80 grams of coffee.

Both of them are measurable numerical
statements.

In the context of project risk, we use
this kind of data to analyze the

impact of all risks on project
objectives, such as total cost,

variance, or potential delays.

So one more time, what is the real
benefit of using quantitative risk

It gives us a clearer picture of our
overall risk exposure and it helps us

better informed decisions about the risk
responses.

Quantitative risk analysis has two main
benefits.

First, it is the only reliable method
for assessing the overall level of

risk because it evaluates the combined
impact of all risks and uncertainties.

Second, it gives us quantitative data
that supports smarter decision -making.

With this data, we can identify which
risks deserve more attention and how we

should respond to them, whether it is
setting aside more budget, adjusting

schedules, or preparing contingency
plans.

But to do this properly, we need a few
things.

First, we need high -quality risk data.

we also need a clear baseline for scope,
schedule, and cost.

And of course, sometimes we even need
specialized software or expert support

build and interpret models.

So when should you actually perform
quantitative risk analysis?

It is best suited for large or complex
projects, projects with contractual

requirements, or where stakeholders
explicitly request it.

You should also consider it when the
project is strategically important for

organization or if it is sensitive to
delays and overruns.

But even in these cases, you need to
check a few things before jumping in.

So say yes to quantitative analysis if
you have the necessary tools and data.

You're confident that most key risks
have been identified.

You have budget and time for the
analysis, and there is low tolerance for

or schedule deviations.

On the other hand, if your data isn't
accurate or subjective inputs work just

well for you, then a qualitative
approach might be more efficient and

-effective.

Here is the big picture of the
quantitative risk analysis process.

On the left, we start with a solid set
of inputs.

including the project management plan,
risk register, and reliable cost and

schedule estimates.

Without these inputs, the analysis
simply won't work.

In the center, we see a range of tools
and techniques used to perform the

analysis. These include expert judgment
and interviews, visual models like

influence diagrams, and most
importantly, simulation tools,

analysis, and decision tree analysis.

We will cover each of these in more
detail in upcoming slides.

Now let's look at the main tools and
techniques used in quantitative risk

analysis. We usually begin with the
expert judgment, which plays a crucial

especially when translating qualitative
insights into numeric estimates or when

interpreting complex results.

Next, we have data gathering, often done
through interviews with SMEs to get

accurate data on risk probabilities,
impact, and dependencies.

Team skills are also vital because they
help ensure the group state focus,

minimize conflict, and support consensus
building during risk workshops.

Then we come to the representation of
uncertainty.

Here, we model uncertainties in things
like cost or time using probability

distributions such as triangular,
normal, or beta distributions.

And finally, we use data analysis
techniques such as simulations to

wide range of possible outcomes,
sensitivity analysis to see which

have the biggest impact,

Decision tree analysis to compare
different options under uncertainty and

influence diagrams to show how variable
and decisions interact visually.

Let's now look at one of the most
powerful tools in quantitative risk

that is Monte Carlo simulation.

This method allows us to evaluate how
uncertainties in cost or time might

the overall project outcomes.

Here is how it works.

We take input values like cost estimates
or activity durations and let the

computer randomly select values from
their defined range.

Then we run the model thousands of times
and what we get as outputs are things

like histograms showing the frequency of
different results such as how often we

finish under budget.

And also S -curves, which gives us the
cumulative probability of meeting a

specific cost or a scheduled target.

For example, if your S -curve shows that
there is only a 60 % chance of

completing project under $10 million,
then it might be a red flag for you.

And also, when we apply simulation to a
scheduled risk, we can do a critical

analysis to find out which activities
are most likely to appear on the

parts of the project.

And here we have decision tree analysis
that is a valuable tool for choosing the

best option when faced multiple project
passes.

Each branch of the tree represents a
decision or a chance event.

For example, in this case, we are
deciding whether to build a new plant or

upgrade the existing one.

The branches continue with possible
future events like a strong or weak

And each endpoint shows the resulting
outcomes that can be either a gain or

loss. Then to evaluate each branch, we
use expected monetary value or EMV,

is the weighted average of all possible
outcomes along that path.

For example, in this case, the decision
to build a new plant leads to higher

EMV. So that becomes the optimal choice.

Now let's walk through this example.

Imagine you are prime contractor of the
project and the contract imposes a $1

,000 penalty per day of late delivery.

Now you have two subcontractor options.

Subcontractor A is the low -cost but
risky option.

They offer a cheaper bid, but there is a
50 % chance of a 90 -day delay, which

could cost you $90 ,000 in penalties.

On the other hand, Subcontractor B is
more expensive but more reliable.

They have only a 10 % chance of being 30
days late, meaning a smaller penalty

that is $30 ,000.

So how do you decide?

You will use Expected Monetary Value or
EMV. For each option, you combine the

original bid with the probability
-weighted penalty to get the expected

cut. Let's see how it works.

Now let's look at how we calculate the
expected monetary value for each option.

First, we start with subcontractor A
that is low but risky choice.

There is a 50 % chance of a $90 ,000
penalty so the total cost could go up to

$200 ,000.

But there is also a 50 % chance of no
delay keeping the total cost at $110

As a result, the EMV here that is the
weighted average will be $155 ,000 for

this subcontractor.

Next, we evaluate the subcontractor B
that is high but reliable one.

With 10 % chance of a 30 -day delay, the
total cost could raise up to $170 ,000.

But in 90 % of the time, there is no
delay and the cost stays at $140 ,000.

So EMV will be $143 ,000 for this
subcontractor.

As a result, although subcontractor B
has a higher bid, its lower risk results

in more favorable expected monetary
value.

Therefore, subcontractor B is a smarter
choice in terms of minimizing expected

cost.

Now let's talk about influence diagrams,
which are powerful tools for analyzing

decisions under uncertainty.

They help us visualize relationships
between three key elements that are

decisions, like whether or not to invest
on certain factors like R &D success

and outcomes such as sales and net
profit.

These diagrams uses arrows to show how
different elements influence each other.

For example, here we see that our R &D
investment decision affects the

likelihood of R &D success, which in
turn influence both sales and net

These models often use probability
distribution to represent uncertainty

evaluated using simulation tools like
Monte Carlo.

FMEA, or failure moods and effects
analysis, is a structured approach that

us identify potential failures in a
process, product, or system, and

how these failures might affect overall
performance.

The key goal of FMEA is to prioritize
risk.

There is done using formula called risk
priority number or RPN.

RPN multiplies three factors that are
severity or how serious the impact of

would be, occurrence or how likely it is
to happen, and detection or how likely

we are to notice the risk before it
happens.

Each of these factors is scored on a
scale from 1 to 10 and by multiplying

scores together we get an RPN between 1
and 1000.

Higher RPN values mean more critical
risks which should be addressed first.

Here we apply the FMEA technique to a
real world ID project that is the

implementation of a new CRM system.

Imagine during the risk identification
phase the team highlighted three major

risks. that are inadequate training for
end users, delays in server delivery by

the supplier, and data transfer errors
from the legacy system.

For each risk, we assessed the severity,
occurrence, and detectability on a

scale from 1 to 10 and then calculated
the risk priority number.

The results show that data transfer
errors had the highest RPN of 315.

This means it is the most critical risk
and should be addressed first in our

mitigation plans.

The other two risks that are inadequate
training with an RPN of 210 and server

delay with an RPN of 96 are still
important but with lower priority.

One of the most important outcomes of
quantitative risk analysis is the risk

report. This report gives us a clear
data -driven picture of the project's

overall exposure to risk using both
numerical results and narrative

It also helps us update the key project
documents including the overall risk

exposure, detailed probabilistic
analysis, and a prioritized list of

risks. In addition, we can track trends
across analysis results and determine

which risk response are most
appropriate.

Before our next session, please make
sure to complete the following readings.

Start with chapters 7 and 9 from
Kendrick and also review sections 11 .3

.4 from the PEMBOX 6th edition.

To reinforce your learning, please
complete the following activities.

First, reply to at least two of your
classmates' posts on Discussion Board 2.

Then take Quiz 3, complete Problem 1,
and submit Project Milestone 3.

Finally, please take a few minutes to
review the Case Study 4.

And that brings us to the end of Part B
of our session on Quantitative Risk

Analysis. If you have any questions,
please don't hesitate to reach out. You

also email me.

Thank you very much for watching this
video